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06/26/2023
Scalable Neural Contextual Bandit for Recommender Systems
High-quality recommender systems ought to deliver both innovative and re...
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05/19/2023
Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models
A centerpiece of the ever-popular reinforcement learning from human feed...
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05/05/2023
Bayesian Reinforcement Learning with Limited Cognitive Load
All biological and artificial agents must learn and make decisions given...
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02/23/2023
A Definition of Non-Stationary Bandits
The subject of non-stationary bandit learning has attracted much recent ...
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02/18/2023
Approximate Thompson Sampling via Epistemic Neural Networks
Thompson sampling (TS) is a popular heuristic for action selection, but ...
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02/07/2023
Leveraging Demonstrations to Improve Online Learning: Quality Matters
We investigate the extent to which offline demonstration data can improv...
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12/24/2022
Inclusive Artificial Intelligence
Prevailing methods for assessing and comparing generative AIs incentiviz...
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12/02/2022
An Information-Theoretic Analysis of Compute-Optimal Neural Scaling Laws
We study the compute-optimal trade-off between model and training data s...
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11/29/2022
Posterior Sampling for Continuing Environments
We develop an extension of posterior sampling for reinforcement learning...
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11/03/2022
Fine-Tuning Language Models via Epistemic Neural Networks
Large language models are now part of a powerful new paradigm in machine...
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10/30/2022
On Rate-Distortion Theory in Capacity-Limited Cognition Reinforcement Learning
Throughout the cognitive-science literature, there is widespread agreeme...
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09/18/2022
Is Stochastic Gradient Descent Near Optimal?
The success of neural networks over the past decade has established them...
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07/01/2022
Robustness of Epinets against Distributional Shifts
Recent work introduced the epinet as a new approach to uncertainty model...
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06/08/2022
Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping
In machine learning, an agent needs to estimate uncertainty to efficient...
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06/04/2022
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning
The quintessential model-based reinforcement-learning agent iteratively ...
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06/04/2022
Between Rate-Distortion Theory Value Equivalence in Model-Based Reinforcement Learning
The quintessential model-based reinforcement-learning agent iteratively ...
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05/04/2022
Nonstationary Bandit Learning via Predictive Sampling
We propose predictive sampling as an approach to selecting actions that ...
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03/02/2022
An Analysis of Ensemble Sampling
Ensemble sampling serves as a practical approximation to Thompson sampli...
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03/01/2022
Sample Complexity versus Depth: An Information Theoretic Analysis
Deep learning has proven effective across a range of data sets. In light...
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02/28/2022
Evaluating High-Order Predictive Distributions in Deep Learning
Most work on supervised learning research has focused on marginal predic...
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01/06/2022
Gaussian Imagination in Bandit Learning
Assuming distributions are Gaussian often facilitates computations that ...
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10/26/2021
The Value of Information When Deciding What to Learn
All sequential decision-making agents explore so as to acquire knowledge...
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10/09/2021
Evaluating Predictive Distributions: Does Bayesian Deep Learning Work?
Posterior predictive distributions quantify uncertainties ignored by poi...
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09/26/2021
Deep Exploration for Recommendation Systems
We investigate the design of recommendation systems that can efficiently...
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07/20/2021
Evaluating Probabilistic Inference in Deep Learning: Beyond Marginal Predictions
A fundamental challenge for any intelligent system is prediction: given ...
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07/19/2021
Epistemic Neural Networks
We introduce the epistemic neural network (ENN) as an interface for unce...
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03/06/2021
Reinforcement Learning, Bit by Bit
Reinforcement learning agents have demonstrated remarkable achievements ...
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02/18/2021
A Bit Better? Quantifying Information for Bandit Learning
The information ratio offers an approach to assessing the efficacy with ...
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02/10/2021
Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent State
We design a simple reinforcement learning agent that, with a specificati...
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01/15/2021
Deciding What to Learn: A Rate-Distortion Approach
Agents that learn to select optimal actions represent a prominent focus ...
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10/05/2020
Randomized Value Functions via Posterior State-Abstraction Sampling
State abstraction has been an essential tool for dramatically improving ...
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06/12/2020
Hypermodels for Exploration
We study the use of hypermodels to represent epistemic uncertainty and g...
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02/17/2020
Langevin DQN
Algorithms that tackle deep exploration – an important challenge in rein...
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12/13/2019
Provably Efficient Reinforcement Learning with Aggregated States
We establish that an optimistic variant of Q-learning applied to a finit...
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11/21/2019
Information-Theoretic Confidence Bounds for Reinforcement Learning
We integrate information-theoretic concepts into the design and analysis...
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11/18/2019
Comments on the Du-Kakade-Wang-Yang Lower Bounds
Du, Kakade, Wang, and Yang recently established intriguing lower bounds ...
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08/09/2019
Behaviour Suite for Reinforcement Learning
This paper introduces the Behaviour Suite for Reinforcement Learning, or...
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05/12/2019
On the Performance of Thompson Sampling on Logistic Bandits
We study the logistic bandit, in which rewards are binary with success p...
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05/30/2018
An Information-Theoretic Analysis for Thompson Sampling with Many Actions
Information-theoretic Bayesian regret bounds of Russo and Van Roy captur...
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05/30/2018
An Information-Theoretic Analysis of Thompson Sampling for Large Action Spaces
Information-theoretic Bayesian regret bounds of Russo and Van Roy captur...
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05/23/2018
Scalable Coordinated Exploration in Concurrent Reinforcement Learning
We consider a team of reinforcement learning agents that concurrently op...
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03/07/2018
Satisficing in Time-Sensitive Bandit Learning
Much of the recent literature on bandit learning focuses on algorithms t...
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02/05/2018
Coordinated Exploration in Concurrent Reinforcement Learning
We consider a team of reinforcement learning agents that concurrently le...
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08/29/2017
Learning to Price with Reference Effects
As a firm varies the price of a product, consumers exhibit reference eff...
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06/13/2017
On Optimistic versus Randomized Exploration in Reinforcement Learning
We discuss the relative merits of optimistic and randomized approaches t...
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05/20/2017
Ensemble Sampling
Thompson sampling has emerged as an effective heuristic for a broad rang...
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03/22/2017
Deep Exploration via Randomized Value Functions
We study the use of randomized value functions to guide deep exploration...
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02/14/2017
Gaussian-Dirichlet Posterior Dominance in Sequential Learning
We consider the problem of sequential learning from categorical observat...
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11/19/2016
Conservative Contextual Linear Bandits
Safety is a desirable property that can immensely increase the applicabi...
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08/09/2016
